Source code for mmlspark.stages.UDFTransformer
# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.
import sys
if sys.version >= '3':
basestring = str
from pyspark.ml.param.shared import *
from mmlspark.stages._UDFTransformer import _UDFTransformer
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.common import inherit_doc
from pyspark.sql.functions import UserDefinedFunction
from pyspark.ml.common import inherit_doc
from mmlspark.core.schema.Utils import *
[docs]@inherit_doc
class UDFTransformer(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
inputCol (str): The name of the input column (default: )
outputCol (str): The name of the output column
udf (object): User Defined Python Function to be applied to the DF input col
udfScala (object): User Defined Function to be applied to the DF input col
"""
@keyword_only
def __init__(self, inputCol=None, inputCols=None, outputCol=None, udf=None):
super(UDFTransformer, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.stages.UDFTransformer")
self.inputCol = Param(self, "inputCol", "inputCol: The name of the input column (default: )")
self.inputCols = Param(self, "inputCols", "inputCols: The names of the input columns (default: )")
self.outputCol = Param(self, "outputCol", "outputCol: The name of the output column")
self.udf = Param(self, "udf", "udf: User Defined Python Function to be applied to the DF input col")
if udf != None:
self.setUDF(udf)
self._udf = udf
if inputCol != None:
self.setInputCol(inputCol)
if inputCols != None:
self.setInputCols(inputCols)
if outputCol != None:
self.setOutputCol(outputCol)
[docs] def setInputCol(self, value):
"""
Args:
inputCol (str): The name of the input column (default: )
"""
self._set(inputCol=value)
return self
[docs] def getInputCol(self):
"""
Returns:
str: The name of the input column (default: )
"""
return self.getOrDefault(self.inputCol)
[docs] def setInputCols(self, value):
"""
Args:
inputCols (list): The names of the input columns (default: )
"""
self._set(inputCols=value)
return self
[docs] def getInputCols(self):
"""
Returns:
str: The name of the input column (default: )
"""
return self.getOrDefault(self.inputCols)
[docs] def setOutputCol(self, value):
"""
Args:
outputCol (str): The name of the output column
"""
self._set(outputCol=value)
return self
[docs] def getOutputCol(self):
"""
Returns:
str: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def setUDF(self, udf):
name = getattr(udf,"_name", getattr(udf, "__name__", None))
name = name if name else udf.__class__.__name__
userDefinedFunction = UserDefinedFunction(udf.func, returnType = udf.returnType, name = name)
self._java_obj = self._java_obj.setUDF(userDefinedFunction._judf)
self._udf = udf
return self
[docs] @classmethod
def read(cls):
""" Returns an MLReader instance for this class. """
return JavaMMLReader(cls)
[docs] @staticmethod
def getJavaPackage():
""" Returns package name String. """
return "com.microsoft.ml.spark.stages.UDFTransformer"
@staticmethod
def _from_java(java_stage):
module_name=_UDFTransformer.__module__
module_name=module_name.rsplit(".", 1)[0] + ".UDFTransformer"
return from_java(java_stage, module_name)